A Survey on Opinion Reason Mining and Interpreting Sentiment Variations
Tracking social media sentiment on a desired target is certainly an important query for many decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2021
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2986 https://doi.org/10.1109/ACCESS.2021.3063921. |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980615397179392 |
|---|---|
| author | ALATTAR, FUAD |
| author2 | SHAALAN, KHALED |
| author2_role | author |
| author_facet | ALATTAR, FUAD SHAALAN, KHALED |
| author_role | author |
| dc.creator.none.fl_str_mv | ALATTAR, FUAD SHAALAN, KHALED |
| dc.date.none.fl_str_mv | 2021 2025-05-13T13:06:57Z 2025-05-13T13:06:57Z |
| dc.identifier.none.fl_str_mv | Alattar, F. and Shaalan, K. (2021) “A Survey on Opinion Reason Mining and Interpreting Sentiment Variations,” IEEE Access, 9. 2169-3536 https://bspace.buid.ac.ae/handle/1234/2986 https://doi.org/10.1109/ACCESS.2021.3063921. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | IEEE Accessv9 (2021): 39636-39655 |
| dc.subject.none.fl_str_mv | Emerging topic, event detection, interpreting sentiment variations, opinion reason mining, sentiment analysis, sentiment reasoning, sentiment spikes, topic modeling |
| dc.title.none.fl_str_mv | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| dc.type.none.fl_str_mv | Article |
| description | Tracking social media sentiment on a desired target is certainly an important query for many decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing subjective texts further to understand possible motives behind extracted sentiments. Few other studies took several steps ahead by attempting to automatically interpret sentiment variations. Learning reasons from sentiment variations is indeed valuable, to either take necessary actions in a timely manner or learn lessons from archived data. However, machines are still immature to carry out the full Sentiment Variations’ Reasoning task perfectly due to various technical hurdles. This paper attempts to explore main approaches to Opinion Reason Mining, with focus on Interpreting Sentiment Variations. Our objectives are investigating various methods for solving the Sentiment Variations’ Reasoning problem and identifying some empirical research gaps. To identify these gaps, a real-life Twitter dataset is analyzed, and key hypothesis for interpreting public sentiment variations are examined. |
| id | budr_26d007314bdaf08c0847d81dfa94b402 |
| identifier_str_mv | Alattar, F. and Shaalan, K. (2021) “A Survey on Opinion Reason Mining and Interpreting Sentiment Variations,” IEEE Access, 9. 2169-3536 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2986 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Survey on Opinion Reason Mining and Interpreting Sentiment VariationsALATTAR, FUADSHAALAN, KHALEDEmerging topic, event detection, interpreting sentiment variations, opinion reason mining, sentiment analysis, sentiment reasoning, sentiment spikes, topic modelingTracking social media sentiment on a desired target is certainly an important query for many decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing subjective texts further to understand possible motives behind extracted sentiments. Few other studies took several steps ahead by attempting to automatically interpret sentiment variations. Learning reasons from sentiment variations is indeed valuable, to either take necessary actions in a timely manner or learn lessons from archived data. However, machines are still immature to carry out the full Sentiment Variations’ Reasoning task perfectly due to various technical hurdles. This paper attempts to explore main approaches to Opinion Reason Mining, with focus on Interpreting Sentiment Variations. Our objectives are investigating various methods for solving the Sentiment Variations’ Reasoning problem and identifying some empirical research gaps. To identify these gaps, a real-life Twitter dataset is analyzed, and key hypothesis for interpreting public sentiment variations are examined.IEEE2025-05-13T13:06:57Z2025-05-13T13:06:57Z2021ArticleAlattar, F. and Shaalan, K. (2021) “A Survey on Opinion Reason Mining and Interpreting Sentiment Variations,” IEEE Access, 9.2169-3536https://bspace.buid.ac.ae/handle/1234/2986https://doi.org/10.1109/ACCESS.2021.3063921.enIEEE Accessv9 (2021): 39636-39655oai:bspace.buid.ac.ae:1234/29862025-05-13T13:11:46Z |
| spellingShingle | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations ALATTAR, FUAD Emerging topic, event detection, interpreting sentiment variations, opinion reason mining, sentiment analysis, sentiment reasoning, sentiment spikes, topic modeling |
| title | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| title_full | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| title_fullStr | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| title_full_unstemmed | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| title_short | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| title_sort | A Survey on Opinion Reason Mining and Interpreting Sentiment Variations |
| topic | Emerging topic, event detection, interpreting sentiment variations, opinion reason mining, sentiment analysis, sentiment reasoning, sentiment spikes, topic modeling |
| url | https://bspace.buid.ac.ae/handle/1234/2986 https://doi.org/10.1109/ACCESS.2021.3063921. |